Systems Security Engineering: What Every System Engineer Needs to Know
Bibliographic record
Abstract
Abstract This paper addresses System Security Engineering (SSE) roles and responsibilities in a framework concept. The framework is a tool to be used with existing guidance for SSE and System Engineering (SE); and, to demonstrate that program protection is not just the responsibility of any one engineer or discipline, it is the responsibility of an entire team. SSE is a specialty engineering discipline of SE. The SSE discipline provides the security approach to SE processes, activities, tasks, products, and artifacts for engineering trustworthy and resilient secure systems. The authors working this project for the INCOSE SSE working group have extensive international experience and exposure including the UK and Australia and have focused their efforts on the international perspective to ensure its relevance and value to the broader international SE community. The framework presented should be leveraged by SEs so they can better understand and integrate SSE into their overall SE processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.017 | 0.038 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".